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In this work, Schottky diodes based on amorphous indium-gallium-zinc-oxide (IGZO) were fabricated on cellulose microfiber paper substrate. Silver lines used as the Schottky barrier were printed, in parallel to thermally evaporated Cr/Au ohmic contact, using a dispense printer. The morphological and electrical characteristics of the devices are presented. The fabricated diodes exhibited rectification ratios ranging from 3.4 to 34.9 at ±1V with ON voltages that range from 1.1V to 1.4 V, for device lengths (Ag to Au distance) from 145 µm to 894 µm. The diodes were characterized in a temperature range between 25°C and 80°C. They showed a decrease of the ON current when increasing temperature, which is mainly attributed to the change of the cellulose microstructure. Indeed, an opposite of the ON current behavior was registered when the diode was realized on a polyimide substrate. The realized flexible paper-based diodes offer a potential promising choice for printed environmental-friendly electronics.

The European Union (EU) regulations require the pilots of Unmanned Aircraft System (UAS) to register and pass a basic theory exam from 2021. Therefore, it is essential to systematically gather information for pilots for utilizing UAS in a safe and meaningful manner especially in Nordic weather conditions. This book chapter describes the Nordic challenges for UAS operations. These can be categorized into two main categories: technological and operational. Based on the extensive literature review and practical experience of the authors, both categories of challenges especially with relevance to the severe Arctic weather conditions are presented. The relevant weather conditions include changing the density of the air, dust and solid particles clouds, extreme light conditions, freezing rain, heavy and gusty wind, heavy clouds, ice fog, rain, mist and fog combinations, rapid temperature changes, snow, storm and hailstorm, temperature crossing 0 C several times within 24 hours, wind shear and whirlwind. Many technical and operational challenges (e.g., weather-related phenomena) are overlapping on both categories and can be mitigated partly by technological advances and partly by operational preparedness. Finally, the future challenges and needs in UAS research are also discussed.

Image classification needs to consider image degradations in practice because an image classification network trained with clean images works poorly for degraded images while digital images usually include some degradations such as JPEG compression. To tackle this problem, a common approach is training classification networks on degraded images with various levels of degradation, e.g. various quality factors for JPEG compression. However, the classification networks do not usually have enough accuracy for clean images because the classification networks have been averagely trained for various levels of degradation. This paper aims to construct a classification network of degraded images without sacrificing the performance of clean images. This paper proposes a network to learn the classification of degraded images and degradation levels of degraded images as multi-task learning. Learning the classification of degraded images is the main task of multi-task learning. On the other hand, learning degradation levels is a sub-task of multi-task learning and reinforces the classification ability. In the proposed network, a feature extractor of the classification is trained with image features acquired from a classification network of clean images by using consistency regularization with the cosine similarity loss. The experimental results using different types of degradations, including JPEG compression, Gaussian blur, Gaussian noise, and salt-and-pepper noise, show that our proposed network has enough ability to classify degraded images without sacrificing the performance for clean images.

The fifth-generation (5G) mobile system is now being deployed across the world and the scale of 5G subscribers is growing quickly in many countries. The attention of academia and industry is increasingly shifting towards the sixth generation (6G) and many pioneering works are being kicked off, indicating an important milestone in the history of 6G. At this juncture, an overview of the current state of the art of 6G research and a vision of future communications are of great interest. This paper thus investigates up-to-date 6G research programs, ambitions, and main viewpoints of representative countries, institutions and companies worldwide. Then, the key technologies are outlined and a vision on ``What 6G may look like?" is provided. This paper aims to serve as an enlightening guideline for interested researchers to quickly get an overview when kicking off their 6G research.

This paper analyzes the behavior of transient switching silicon carbide devices influenced by the parasitics of printed circuit board and the component itself. The parasitics in power and gate driver loops are described and investigated. Double pulse test simulations for silicon carbide devices are conducted in this report and verified through the circuit simulation. The parasitics in the printed circuit board are added based on the real experience printed circuit board design and the parasitics in silicon carbide devices are added based on the manufacturer modelling. The parasitic inductances in the simulation are varied +/- 30% to understand the effect of the parasitics. The drain and source parasitic inductances and the gate parasitic resistance are simulated separately to understand the individual effect on the ringing of silicon carbide devices. The ringing impact on the reliability of devices is also analyzed in this paper.

In the era of information society and 5G networks, cars are extremely important mobile information carriers. In order to meet the needs of multi-scenario business requirements such as vehicle assisted driving and in-vehicle entertainment, cars need to interact with the outside world. This interconnection and data transmission process is usually called vehicular communication (V2X, Vehicle-to-Everything). Device-to-device (D2D) communication not only has partial nature of communication, but also alleviate the current problem of spectrum scarcity of resources. The application of D2D communication in V2X can meet the requirements of high reliability and low latency, but resource reuse also brings interference. Software-defined networking (SDN) provides an optimal solution for interoperability and flexibility between the V2X and D2D communication. This paper reviews the integration of D2D and V2X communication from the perspective of SDN. The state-of-the-art and architectures of D2D-V2X were discussed. The similarity, characteristics, routing control, location management, patch scheduling and recovery is described. The integrated architecture reviewed in this paper can solve the problems of routing management, interference management and mobile management. It also overcome the disconnection problem between the D2D-V2X in terms of SDN and provides some effective solutions. - Qatar National Research Fund (QNRF) - [UREP27-020-1-003]. - Ministry of Higher Education, Malaysia (MOHE) - [FRGS/1/2018/ICT02/UKM/02/6]. - National Research Foundation of Korea (NRF) - [2019R1C1C1007277]. - Taif University (TU)- [TURSP-2020/260].

End users have become active participants in local electricity market transactions because of the growth of the smart grid concept and energy storage systems (ESS). This participation is optimized in this article using a stochastic two-stage model considering the day-ahead and real-time electricity market data. This model optimally schedules the operation of a Smart Home (SH) to meet its energy demand. In addition, the uncertainty of wind and photovoltaic (PV) generation is considered along with different appliances. In this paper, the participation of an EV (electric vehicle), together with the battery energy storage systems, which allow for the increase in bidirectional energy transactions are considered. Demand Response (DR) programs are also incorporated which consider market prices in real-time and impact the scheduling process. A comparative analysis of the performance of a smart home participating in the electricity market is carried out to determine an optimal DR schedule for the smart homeowner. The results show that the SH’s participation in the real-time pricing, scheme not only reduces the operating costs but also leads to better than expected profits. Moreover, total, day-ahead, and real-time expected profits are better in comparison with existing literature. The objective of this paper is to analyze the SH performance within the electrical market context so as to increase the system’s flexibility whilst optimizing DR schedules that can mitigate the variability of end-users generation and load demand.

Since its inception in 2015, Style Transfer has focused on texturing a content image using an art exemplar. Recently, the geometric changes that artists make have been acknowledged as an important component of style[42], [55], [62], [63]. Our contribution is to propose a neural network that, uniquely, learns a mapping from a 4D array of inter-feature distances to a non-parametric 2D warp field. The system is generic in not being limited by semantic class, a single learned model will suffice; all examples in this paper are output from one model.Our approach combines the benefits of the high speed of Liu et al. [42] with the non-parametric warping of Kim et al. [55]. Furthermore, our system extends the normal NST paradigm: although it can be used with a single exemplar, we also allow two style exemplars: one for texture and another geometry. This supports far greater flexibility in use cases than single exemplars can provide.

Crowdsourcing plays an important role in modern IT landscape, enabling the use of human information processing abilities to solve problems that are still hard for machines. One of the specific (and most demanded) applications of crowdsourcing is collecting item annotations, i.e., describing the contents of complex items with a help of labels (tags). Input received from crowdsourcing participants is typically unreliable, therefore, to increase the quality of annotations, each item is typically processed by several participants and the obtained annotations have to be aggregated. The paper considers a special case of annotating, where a set of possible labels, as well as the set of relationships between the labeled items and the labels are defined by an OWL 2 ontology (OWL QL). Such semantic item annotations turn out to be very useful in organizing large collections of items and enabling semantic search in them. In order to increase annotations quality, the paper proposes two aggregation methods - Onto Voting and OntoSB, differing in that the first one is agnostic with respect to participants' reliability and the second one accounts for variations in reliability. Simulation experiments with ontology-based annotations of varying quality show that the proposed aggregation methods increase the quality of collected ontology-based item annotations.

Electricity network modelling and grid simulations form a key enabling element for the integration of newer and cleaner technologies such as renewable energy generation and electric vehicles into the existing grid and energy system infrastructure. This paper reviews the models of the African electricity systems and highlights the gaps in the open model landscape. Using PyPSA (an open Power System Analysis package), the paper outlines the pathway to a fully open model and data to increase the transparency in the African electricity system planning. Optimisation and modelling can reveal viable pathways to a sustainable energy system, aiding strategic planning for upgrades and policy-making for accelerated integration of renewable energy generation and smart grid technologies such as battery storage in Africa.

Great Britain has reached high penetrations of intermittent Distributed Generation (DG), which has influenced many aspects of power system planning and operation. There has been a lack of technical requirements for DG to provide grid support and for network operators to monitor, control and gather detailed information of DG installations. The work reported in this paper has explored available data sources to provide better visibility of the amount, size and type of DG installed at various locations in the distribution networks and evaluated it alongside whole electricity system trends. Several of the issues associated with a high volume of generation being unobservable and uncontrollable are surveyed and discussed, such as active network management and coordination of transmission and distribution (T & D) networks. Finally, the paper discusses system resilience, emphasising that DG loss-of-mains protection continues to present a threat to system security and DG adds uncertainty to the effectiveness of automatic defence mechanisms. The latter issues were brought into focus following a system disturbance in GB on August 9th, 2019 and are reflected on in this study.

© 2021 IEEE.Coronavirus usually transmits from the animal to the human, but now, the virus transmission is between persons. Therefore, scientists and researchers are trying to develop several types of machine learning methods to defend against COVID-19. Medical images play a significant role in this time due to they can be used to recognize COVID-19 accurately. However, in this paper, we used X-Ray images, the images undergone to sharpening techniques to increase the results further. The texture techniques named local binary pattern (LBP) have been used in order to extract features. The features obtained were applied to the support vector machine (SVM). The results we achieved were 100% for all performance measurements. Our results were conspicuously superior compared to the state-of-the-art papers published.

This paper presents a hybrid technique of combining the BERT embedding method and the graph convolutional neural network. This combination is then employed to solve the text classification problem. Initially, we apply the BERT embedding method to the whole corpus in order to transform all the texts into numerical vectors. Then, the graph convolutional neural network will be applied to these numerical vectors to classify these texts into their appropriate classes. Especially, in our approach, we need only a few labeled texts for the model training. For the illustration, in this paper, we use the BBC news and the IMDB movie reviews datasets to perform our experiments, showing that the performance of the graph convolutional neural network model is better than the performances of the combination of the BERT embedding method with other classical machine learning models.

The power distribution grid is centrally managed concerning the requirements of the end-users, however, with the appearance of smart grids, new technologies arc arising. Therefore, distributed energy resources, mainly, renewables, energy storage systems, electric mobility, and power quality are viewed as encouraging contributions for improving power management. In these circumstances, this paper presents a power electronics perspective for the power distribution grid, considering innovative features, and including a power quality perception. Throughout the paper are presented relevant concepts for a concrete realization of a smart grid, supported by the integration of power electronics devices as the interface of the mentioned technologies. Aiming to support the innovative power electronics systems for interfacing the mentioned technologies in smart grids, a set of developed power electronics equipment was developed and, along with the paper, are shown and described, supporting the most important contributions of this paper., This work has been supported by FCT -Fundação para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. This work has been supported by the FCT Project newERA4GRIDs PTDC/EEI-EEE/30283/2017.

The general aim of a cognitive Cyber Physical System of Systems (CPSoS) is to provide managed access to data in a smart fashion such that sensing and actuation capabilities are connected. Whilst there is significant funding and research devoted to this area, focus remains purely on creating bespoke systems. This paper presents a novel approach, based on a set of components to leverage Situational Awareness and Smart Actuation in large manufacturing industries with the focus on enabling predictive maintenance for asset and abnormal situation management. This paper presents a novel generic platform, named AtiCoS, that combines case-based and common-sense reasoning, as the enabling methodologies for enhancing CPSoS with cognitive capabilities.

The power consumption in data centers continues increasing, thereby pushing the converter to achieve high efficiency and high power density. This paper presents an asymmetrical half-bridge flyback converter for data center applications. Compared with an active clamp flyback converter, it functions as an isolated buck converter, and is more preferable for voltage step-down applications. The operating principle and the design considerations are included in this paper. An interleaving winding layout with uniformed current distributions is used for the planar transformer design. The built prototype satisfies an industrial standard low-profile sixteenth brick requirement, and achieves 94.9% peak efficiency and 468W/inch3 power density.

In this paper, we propose an explainable approach for hate speech detection from the under-resourced Bengali language, which we called DeepHateExplainer. In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates using a neural ensemble method of transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased/uncased, and XLM-RoBERTa). Subsequently, important (most and least) terms are identified using sensitivity analysis and layer-wise relevance propagation (LRP), before providing human-interpretable explanations11To foster reproducible research, we make available the data, source codes, models, and notebooks: https://github.com/rezacsedu/DeepHateExplainer. Finally, we compute comprehensiveness and sufficiency scores to measure the quality of explanations w.r.t faithfulness. Evaluations against machine learning (linear and tree-based models) and neural networks (i.e., CNN, Bi-LSTM, and Conv-LSTM with word embeddings) baselines yield F1-scores of 78%, 91%, 89%, and 84%, for political, personal, geopolitical, and religious hates, respectively, outperforming both ML and DNN baselines22Read an extended version of this paper: https://arxiv.org/abs/2012.14353.

Pocket Beach (PB) planview geometry analysis is one of the most important steps needed for PB characterization and dynamic evaluation. With modern technology, quantitative morphology of a PB can be obtained with a high degree of confidence. In this paper we used Unmanned Aerial Vehicle (UAV) photogrammetry-based mapping technology combining drone and Global Positioning System (GPS) for 2D and 3D models’ reconstruction of Isola Bella bay PBs located along the coast of the town of Taormina (eastern Sicily). The aim of this paper is to show how very high spatial resolution data can be obtained and analyzed for the retrieval of geomorphological features and geometric parameters of PBs. A computer-assisted visual interpretation approach was adopted to manually digitize, delineate and extract the planform characteristics of the PB. The planview geometry analysis was conducted on orthophotos and digital surface models of 1.81 cm and 1.69 cm spatial resolutions, acquired during two flight surveys. The mapping approach adopted in this paper shows that information on planform geometry of PBs can be obtained faster using modern remote sensing technologies allowing time series analysis of planview parameters. &nbsp

Under the light of increasing air traffic congestion and reduced environmental impacts, the growing need for tailored approaches has arisen. To coordinate tailored approaches, accurate predictions of descent trajectories must be available. The ability of ground based automation for such endeavours however must be improved. Specifically in the ability to predict where descents begin, also referred to as the Top of descent (TOD). This paper attempts to provide a machine learning approach to TOD location prediction, considering the operational capabilities of modern-day air traffic management by working within the confines of parameters available to controllers and considerations of stakeholders. To identify the common TOD clusters and the resultant average TOD locations representing the controllers macroscopic perspective, an unsupervised learning approach is utilised. Specifically, an ensemble method of DBSCAN-K-means is used. This was compared to the more microscopic pilot controllers perspective and generic decision-making formulas to determine the proximity to expected TOD. Of which the microscopic option has proven to be more reliable in representing the individual Flight Management System (FMS) recommendations or pilots’ decisions on aircraft. In the prediction of TOD location, operationally available factors such as proximity to expected TOD, current flight level, approach heading, ground speed, fleet and aircraft type are used to train a decision tree model. A Singapore flight information region (FIR) case study is conducted to illustrate the methodology. Results of the methodology employed on the inbound A380 fleet consisting a total of 79 flights, yielded a maximum prediction range of 31NM range from actual TOD occurrence. This paper has demonstrates the possibility of using a machine learning approach to predict TOD of a flight considering both prior cruise characteristics and operational considerations of pilots. Accepted version

Fifth Generation (5G) systems are envisaged to support a wide range of applications scenarios with varying requirements. 5G architecture includes network slicing abilities which facilitate the partitioning of a single network infrastructure on to multiple logical networks, each tailored to a given use case, providing appropriate isolation and Quality of Service (QoS) characteristics. Radio Access Network (RAN) slicing is key to ensuring appropriate QoS over multiple domains; achieved via the configuration of multiple RAN behaviours over a common pool of radio resources. This Paper proposes a novel solution for efficient resource allocation and assignment among a variety of heterogeneous services, to utilize the resources while ensuring maximum QoS for network services. First, this paper evaluates the effectiveness of different wireless data bearers. Secondly, the paper proposes a novel dynamic resource allocation algorithm for RAN slicing within 5G New Radio (NR) networks utilising cooperative game theory combined with priority-based bargaining. The impact of this work to industry is to provide a new technique for resource allocation that utilizes cooperative bargaining to ensure all network services achieve minimum QoS requirements – while using application priority to reduce data transfer time for key services to facilitate decreased turnaround time at the gate.

The Publisher's final version can be found by following the DOI link. Data quality plays a crucial role in the context of smart buildings. Meanwhile, missing data is relatively common in acquired datasets from sensors within the smart buildings. Poor data could result in a big bias in forecasting, control and operational services. Despite the common techniques to handle missing data, it is essential to systematically select the most appropriate approach for such missing values. This paper aims to focus on the lift systems as one of the essential parts in the smart buildings by exploring the most appropriate data imputation methods to handle missing data and to provide its service and allow a better understanding of patterns to issue the correct control actions based on forecasted models. The imputed data is not only investigated statistically but also modelled through machine learning algorithm to explore the impact of selecting inappropriate imputation techniques. Seven imputation techniques deployed on datasets with three level of missing values including 10%, 20% and 30% and the performance of methods examined through the normalized root mean square error (NRMSE) approach. In addition, the interaction between imputation techniques and a machine learning algorithm, namely random forest were examined. Findings from this paper can be employed in identifying an appropriate imputation technique not only within the lift datasets, but smart building context.

This paper presents an experimental study of the softlanding problem in quadrotors using the induced wind modeling approach. The landing phase has been typically one of the critical phases in drone flight. Landing complexity drastically increases when the drone needs to land on sensitive sites, such as platforms or rack of pipes in refineries (for inspection purposes), in which explosive material is running through or there exist flammable/explosive material in the environment. Multirotor unmanned aerial vehicles (UAVs) are usually lightweight platforms and they are significantly disturbed by the aerodynamic ground effect while landing; so, near the ground, those drones are subjected to an external disturbance in proximity to the ground. In this situation, the airflow can be reflected after reaching the ground, disturbing the performance of the rotors significantly. This paper aims to model the induced wind velocity, caused by the propellers to see and consider the ground effect during the landing. The reduction of the total thrust near the ground provides a smooth landing and avoids bumping. The complex wind modeling formulation and limitation of the commercialized autopilots make the implementation a challenging task. Herein we propose how to incorporate the proposed soft-landing algorithm within an existing UAV autopilot. Experimental results show that the proposed approach successfully replicates the wind modeling which leads to a soft-landing. European Union (UE). H2020 779411 Ministerio de Ciencia, Innovación y Universidades (España) RTI2018-102224-B-I00

Natural experiments are observational studies where the assignment of treatment conditions to different populations occurs by chance "in the wild". Researchers from fields such as economics, healthcare, and the social sciences leverage natural experiments to conduct hypothesis testing and causal effect estimation for treatment and outcome variables that would otherwise be costly, infeasible, or unethical. In this paper, we introduce VAINE (Visualization and AI for Natural Experiments), a visual analytics tool for identifying and understanding natural experiments from observational data. We then demonstrate how VAINE can be used to validate causal relationships, estimate average treatment effects, and identify statistical phenomena such as Simpson's paradox through two usage scenarios., 5 pages, 4 figures, accepted as short paper at IEEE VIS 2021

Ever since the commercial offerings of the Cloud started appearing in 2006, the landscape of cloud computing has been undergoing remarkable changes with the emergence of many different types of service offerings, developer productivity enhancement tools, and new application classes as well as the manifestation of cloud functionality closer to the user at the edge. The notion of utility computing, however, has remained constant throughout its evolution, which means that cloud users always seek to save costs of leasing cloud resources while maximizing their use. On the other hand, cloud providers try to maximize their profits while assuring service-level objectives of the cloud-hosted applications and keeping operational costs low. All these outcomes require systematic and sound cloud engineering principles. The aim of this paper is to highlight the importance of cloud engineering, survey the landscape of best practices in cloud engineering and its evolution, discuss many of the existing cloud engineering advances, and identify both the inherent technical challenges and research opportunities for the future of cloud computing in general and cloud engineering in particular., Comment: author copy/preprint of a paper published in the IEEE International Conference on Cloud Engineering (IC2E 2021)

Although numerous methods have been formalized for handling the technical aspects of developing domain-specific modeling languages (DSMLs), user needs and usability aspects are often addressed in ad hoc manners and late in the development process. Working in this context, this paper presents the development of the customer journey modeling language (CJML), a DSML for modeling service processes from the end-user's perspective. CJML targets a wide and heterogeneous group of users, making it especially challenging regarding usability. This paper describes how an industry-relevant DSML was systematically improved by using a variety of user-centered design techniques in close collaboration with the target group and how their feedback was used to refine and evolve the syntax and semantics of CJML. We also suggest how a service-providing organization may benefit from adopting CJML as a unifying language for documentation purposes, compliance analysis, and service innovation. Finally, we generalize the experience gained into lessons learned and methodological guidelines.

Chest radiograph (CXR) interpretation in pediatric patients is error-prone and requires a high level of understanding of radiologic expertise. Recently, deep convolutional neural networks (D-CNNs) have shown remarkable performance in interpreting CXR in adults. However, there is a lack of evidence indicating that D-CNNs can recognize accurately multiple lung pathologies from pediatric CXR scans. In particular, the development of diagnostic models for the detection of pediatric chest diseases faces significant challenges such as (i) lack of physician-annotated datasets and (ii) class imbalance problems. In this paper, we retrospectively collect a large dataset of 5,017 pediatric CXR scans, for which each is manually labeled by an experienced radiologist for the presence of 10 common pathologies. A D-CNN model is then trained on 3,550 annotated scans to classify multiple pediatric lung pathologies automatically. To address the high-class imbalance issue, we propose to modify and apply "Distribution-Balanced loss" for training D-CNNs which reshapes the standard Binary-Cross Entropy loss (BCE) to efficiently learn harder samples by down-weighting the loss assigned to the majority classes. On an independent test set of 777 studies, the proposed approach yields an area under the receiver operating characteristic (AUC) of 0.709 (95% CI, 0.690-0.729). The sensitivity, specificity, and F1-score at the cutoff value are 0.722 (0.694-0.750), 0.579 (0.563-0.595), and 0.389 (0.373-0.405), respectively. These results significantly outperform previous state-of-the-art methods on most of the target diseases. Moreover, our ablation studies validate the effectiveness of the proposed loss function compared to other standard losses, e.g., BCE and Focal Loss, for this learning task. Overall, we demonstrate the potential of D-CNNs in interpreting pediatric CXRs., Comment: This is a preprint of our paper which was accepted for publication to ICCV Workshop 2021

The informational environment surrounding the Covid-19 pandemic has been widely recognized as fragmented, politicized, and complex [1]. This has resulted in polarized public views regarding the veracity of scientific communication, the severity of the threat posed by the virus, and the necessity of nonpharmaceutical interventions (NPIs) which can slow the spread of infections [2]. This paper describes CovidCommitment.org, an effort toward enhancing NPI adoption through the combination of a social behavioral commitment device and interactive map-based visualizations of localized infection data as tabulated via a 1-hourdrive-time isochrone. This paper describes the system design and presents a preliminary analysis of user behavior within the system.

Nowadays, energy consumption and especially energy saving are important issues. The concern over global warming has increased the need to save energy in many areas of our living community. One way of slowing global warming is to reduce energy consumption and the related emissions. IoT systems can be utilized to monitor living conditions in residential and commercial buildings. These IoT systems continuously produce huge amounts of data. Similarly, various vendor-specific building automation systems generate data on an equal scale. Combining these data sources can be challenging, as protocols and data are seldom in a unified and interoperable format. In this paper, we present the DataSites service, which integrates back-end systems and client-oriented REST APIs. The high-level view of DataSites architecture, how to relay building data to client applications, and the required interfaces are described in this paper. Use of the service is illustrated using two real-life use cases. acceptedVersion

High temporal frequency monitoring of transport infrastructures is crucial for implementing effective maintenance prioritisation strategies and prevent major failures. To this extent, ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards of data quality and accuracy. However, routine and systematic campaigns are required over relatively long inspection times for data collection and implementation into reliable infrastructure management systems (IMSs). On the other hand, satellite remote sensing techniques, such as the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method, have proven effective in monitoring ground displacements of transport infrastructures (roads, railways and airfields), with a much higher temporal frequency of investigation and wider inspection catchment areas. Nevertheless, the integration of information from i) satellite remote sensing and ii) ground-based NDT methods is still an area to be explored in civil engineering. Within this framework, this paper aims to review significant stand-alone applications in these two areas of technology for transport infrastructure monitoring. Furthermore, recent advances, main challenges and future perspectives arising from their integration are discussed. Contents of this paper are organised within the context of an invited keynote talk given at the 2021 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing (AGERS – 2021).

International audience; Modern manufacturing systems are subject to many disruptions due to increased competition. They are subject to many configuration changes during their life cycle and must cost-effectively make these changes. Intelligent componentbased development enables the rapid design of control systems. This paper presents a new methodology for structuring the control architecture of cyber-physical systems with intelligent components: The Hierarchical Intelligent Component Architecture (HICA). Compared to traditional design methods, this methodology integrates the design of the physical and software architecture simultaneously. This paper also describes the model-driven engineering to realize this architecture using SysML diagrams to implement the code in IEC 61499 standard. Diagram Linkage Table (DTL) is the central element that allows the transition from SysML diagrams to IEC 61499.

Disrupted by the COVID-19 pandemic, the 2019/20 academic year brought an unplanned shift to education online. While teaching itself became more challenging, the main disruption was in testing, where not being in the same room with students whose knowledge was being evaluated posed serious challenges. This paper discusses the approach taken by the lecturers of the course „Fundamentals of electrical engineering” in professional study of electrical engineering at the Zagreb University of Applied Sciences. The paper examines the important role of a properly set up Learning management system for knowledge evaluation and compares the unproctored online approach to the typical proctored exam.

In modern software development, the JavaScript ecosystem of various frameworks and libraries used to develop contemporary web applications presents many advantages. JavaScript is a widely known interpreted programming language, simple to learn and start development, and with numerous third-party libraries and extensions. However, with the rise of highly user-interactive websites and browser-based games, in some cases, JavaScripts executable engine could lack in performance. Therefore, developers could combine several other programming languages to create a polyglot user-interactive interoperable system to develop efficient modern web applications. The interoperability modules offer significant advantages but also present challenges in the execution due to high complexity and longer compilation times. This paper explores WebAssembly, a binary format compilation target with a low-level assembly-like language used for targeting from other programming languages. The binary format allows near-native performance level due to its compactness, as it prioritizes usage of low-level languages. Moreover, as a continuation of our previous research of the GraalVM ecosystem, we analyzed a guest language implementation of a WebAssembly based system, TruffleWasm, hosted on GraalVM and Truffle Java framework. This paper presents the architecture and review of the TruffleWasm within the GraalVM-based ecosystem as well as from performance test results within our academic environment., Comment: 6 pages, 3 figures

Higher education institutions (HEIs) face many challenges in an increasingly complex and turbulent environment. Academic managers are becoming leaders of change in their institutions and their roles are increasingly more challenging. Their managers' decisions and decision-making styles (DMS) directly affect the organization's efficiency, adaptability, employee satisfaction, and student academic performance. Since a DMS of decision maker forms the backbone of effective decision-making (DM), it is extremely important to analyze DMS of an academic managers. Therefore, this paper aimed to present findings of relevant studies on the DMS used in HEIs as well as the consequences of their usage. To emphasize the need of conducting further research on academic managers' DMS in conditions of unstable environment, the systematic literature review (SLR) is conducted and presented in the paper.

The growing demand for photovoltaic (PV)-based buildings and increasing utilization of DC electronic loads, along with decreasing DC appliances prices, have resulted in attraction of DC networks for residential consumers. The main aim of this paper is to present an economic study of a PV system-based DC residential distribution network. This includes designing and simulation of the system that is based on DC residential distribution network. Electrical Transient Analyzer Program (ETAP) software can be used for designing and modelling of the PV system components for a single house and for the distribution grid. The process starts with the PV module, DC/DC converter, battery, and the load. It takes into account all the ratings of the specific components for the whole system. Subsequently, the system efficiency has been evaluated for a common DC home at 24V, 48V, and 110V DC, and compared to the 220V AC through the simulation. Power losses will be through the conversion and distribution. Based on the obtained results using American Wire Gauge AWG-6 for a single house, it is found that the efficiency of a 48V DC system is higher than that of 220V AC system. It is also found that the usage of 48V DC leads to better efficiency compared with that of other system. This states that the efficiency of the system will increase with increasing of the main bus voltage. At the rest of the paper, the installed capacity for a single house is evaluated at 10 KW per house and this is results in a 2 MWp total installed PV system for 200 houses. This installed capacity can produce 4344 GWh per year. The installed PV system could improve the Libyan grid by reducing losses. Moreover, the installed PV capacity saves around 592,956,000 LD yearly and prevents emission of 328.82 million kg, 5,408,890 million kg and, 4963617743 million kg from gas, heavy fuel, and light fuel respectively.

Our paper provides insights into which critical success factors (CSFs) for digitalization projects are seen as important from the companies’ perspective based on an online survey. The results presented in this paper show that CSFs of the dimensions of Corporate organization and Technology are considered to be of particular relevance, as stated by the companies, with Corporate culture, Top management support, and a Unified digital corporate strategy / vision as the three most important CSFs. Therefore, this paper contributes to the CSF research regarding digital transformation and enables the development of practice-oriented recommendations for action and assistance in shaping digital transformation.

This paper presents a model predictive control (MPC) approach to a microgrid at a cotton farm so as to minimize the water pumping operational cost while taking full advantage of renewable energy sources. The reason for using MPC is its ability in handling noise, disturbance, and real-time parameter changes. In this paper, the MPC models of grid-connected are established; moreover, the effectiveness and robustness of the MPC models are analyzed by cotton farm case studies. Simulation results show that the optimal MPC solutions for grid-connected microgrid of a farm are AU$8.4/ML less than a manual control-based strategy. In addition, the MPC solution shows outstanding robustness in controlling the water reservoir level. When the disturbance data of the rainy season in 2016 are added, the system saves 34.5% of the operating cost compared with the baseline. When the rainy season disturbance is added together, the system saves 11.74% of operating costs compared to the baseline.

The integration of renewable energy sources (RESs) into distributed microgrid systems has been widely applied in agriculture, and in particular in cotton farms. Due to the specific irrigation periods and non-irrigation periods during cotton growth, and the inherent intermittent characteristics of RESs, the design of a cotton farm microgrid system becomes challenging. Finding the optimal size of the RESs for a cotton farm microgrid needs to consider not only the energy demand for cotton irrigation but also the investment cost and the payback period. This paper presents an optimization model for cotton farm microgrid design, which explores available RESs and energy storage options to ensure reliable power supply from renewables. Furthermore, the designed microgrid utilizes solar photovoltaic (PV) units and wind turbine generator as RESs together with battery storage and demonstrates the supply and demand relationship between the microgrid and pump loads. By using RES power supply, renewable energy is optimally utilized to satisfy the seasonal loads demand, and the grid power is used as a backup power source. The objectives of optimization include investment cost, operating cost and simple payback period. In order to solve the underlying optimization problem, this paper adopts YALMIP MATLAB Toolbox. A case study is undertaken using historical energy consumption data for a cotton farm in Gunnedah, New South Wales, to verify the applicability of the proposed approach.

This paper focuses on the control of a simulated robot hand using a data glove, which gathers information from resistors that measure the flexion of the user's fingers. The data glove used was a commercially available controller, using 5 flex sensors on each finger to collect data. Arduino Uno R3 microcontroller board was used to power the data glove which is based on the ATmega328P microcontroller. CoppeliaSim was used as a platform for simulation, with a compatible model for the hand found within the software's community. C++ is used within both the CoppeliaSim and Arduino environments, with a micro-USB connecting the two. This paper allows for real time control with the proposed data glove, which transmit the data in the form of a string of 5 different values. In the future, many adjustments and modifications could be made to allow for more precise control and eventually more degrees of freedom. A video demo for the proposed design can be seen from: https://youtu.be/w-GqzigfkXY.

Forest fire weather index (FWI), empirical model for fire risk estimation based on temperature, humidity and wind speed and precipitation, is used by European Forest Fire Information System (EFFIS) and by Croatian Meteorological and Hydrological Service (DHMZ). EFFIS also estimates fires based on satellite images, which is not precise enough for Croatia forest fires because the number actual fires were several times higher number of real fires in southernmost part of Croatia (Dalmatia). The aims of the paper is to examine the possibility of Machine Learning (ML) in identification of forest fire risk by using only meteorological measurements and to compare ML in forest fire risk identification to widely used Fire Weather Index (FWI). Three different ML models have been built and compared - logistic regression, random forest and artificial neural network on two different sets of features on period 2017-2018. The contribution of the paper is the process of ML and FWI models comparison based on actual fires and generated no-fires. The actual fire database have been manually collected from Croatian Fire Brigade website. Our results show that the best model according to F-score is multiple layer perceptron (MLP) with temperature, humidity, wind speed and ultra violet index (UVI) (F-score is 0.78), while FWI model that seems to be good according to F-score is FWI > 11.2 (F- score is 0.7). The disadvantage of both FWI and ML models is that they report ’Fire’ in high percentage of situations in Dalmatia during fire season.

The accelerating development of autonomous driving technology has placed greater demands on obtaining large amounts of high-quality data. Representative, labeled, real world data serves as the fuel for training deep learning networks, critical for improving self-driving perception algorithms. In this paper, we introduce PandaSet, the first dataset produced by a complete, high-precision autonomous vehicle sensor kit with a no-cost commercial license. The dataset was collected using one 360{\deg} mechanical spinning LiDAR, one forward-facing, long-range LiDAR, and 6 cameras. The dataset contains more than 100 scenes, each of which is 8 seconds long, and provides 28 types of labels for object classification and 37 types of labels for semantic segmentation. We provide baselines for LiDAR-only 3D object detection, LiDAR-camera fusion 3D object detection and LiDAR point cloud segmentation. For more details about PandaSet and the development kit, see https://scale.com/open-datasets/pandaset., Comment: This paper has been published on ITSC'2021, please check the website of the PandaSet for more information: https://pandaset.org/

Increasing urbanization and exacerbation of sustainability goals threaten the operational efficiency of current transportation systems and confront cities with complex choices with huge impact on future generations. At the same time, the rise of private, profit-maximizing Mobility Service Providers leveraging public resources, such as ride-hailing companies, entangles current regulation schemes. This calls for tools to study such complex socio-technical problems. In this paper, we provide a game-theoretic framework to study interactions between stakeholders of the mobility ecosystem, modeling regulatory aspects such as taxes and public transport prices, as well as operational matters for Mobility Service Providers such as pricing strategy, fleet sizing, and vehicle design. Our framework is modular and can readily accommodate different types of Mobility Service Providers, actions of municipalities, and low-level models of customers choices in the mobility system. Through both an analytical and a numerical case study for the city of Berlin, Germany, we showcase the ability of our framework to compute equilibria of the problem, to study fundamental tradeoffs, and to inform stakeholders and policy makers on the effects of interventions. Among others, we show tradeoffs between customers satisfaction, environmental impact, and public revenue, as well as the impact of strategic decisions on these metrics., Comment: 8 pages, 6 figures, Published in the Proceedings of the 2021 IEEE International Conference on Intelligent Transportation Systems (Awarded the Best Paper Award - First Place)

Stereo matching is a key component of autonomous driving perception. Recent unsupervised stereo matching approaches have received adequate attention due to their advantage of not requiring disparity ground truth. These approaches, however, perform poorly near occlusions. To overcome this drawback, in this paper, we propose CoT-Stereo, a novel unsupervised stereo matching approach. Specifically, we adopt a co-teaching framework where two networks interactively teach each other about the occlusions in an unsupervised fashion, which greatly improves the robustness of unsupervised stereo matching. Extensive experiments on the KITTI Stereo benchmarks demonstrate the superior performance of CoT-Stereo over all other state-of-the-art unsupervised stereo matching approaches in terms of both accuracy and speed. Our project webpage is https://sites.google.com/view/cot-stereo., 5 pages, 3 figures and 2 tables. This paper is accepted by ICIP 2021

The significance and urgency of detecting the cognitive load of a Visually Impaired Person (VIP) are essential when perception comes while designing an automated navigation aid for them in unfamiliar indoor environments. Our paper presents a novel and robust framework based on the iterative feature pooling technique which recursively selects paramount features that maintains relationships with the change in the cognitive load of the brain. We took the well-established Event-Related Desynchronization and Synchronization (ERDS) method for indexing the cognitive load and further developed the work by operating with the band power of not only the Alpha wave but the Alpha Beta band power ratio and Alpha Theta band power ratio. The supervised machine learning classifier, Gradient Boost outperformed all other classifiers reaching 94% accuracy in the best case. When provided with the most reliable features and proper tuning, this turns out to perform 7% to 8% better than the other classifiers like the Support Vector Machine (SVM), K-nearest neighbor, Naive Bayes, and Multilayer perceptron. We considered some performance parameters like the accuracy, null-accuracy, recall, precision, F1 Score, and False Alarm rate to evaluate the performance of all available supervised Machine learning classifiers. Our paper marks out the estimation of cognitive load based on Electroencephalogram (EEG) signals analysis with the existing literature, background, leeway, features, and machine learning techniques.

In this paper, the open-source Nanospace framework is presented, dedicated to facilitate concurrent engineering during the preliminary design phase of CubeSats. It allows for direct information exchange between third-party expert software, while allowing transparent data visualization to any team member. It provides a web based GUI, and relies on a database with ACID properties and provides a RESTful API. Nanospace ensures concurrent access to the data, which is relevant when teams are working remotely. Nanospace also provides an intuitive way of visualizing other experts’ contributions that can be of high value when looking for internal project understanding and transparency. The paper presents the modular software architecture of Nanospace, as well as design choices and the different options to connect to third-party specialized software and the database.

Modern farming is experiencing increased adoption of mobile and cloud computing applications as efforts are being made to automate farming processes. In this context, the mobile and cloud computing applications, that we refer to as smart agriculture applications (SAAs), can be used in data collection that can be entered directly into the applications by end-users (i.e., farmers) or via sensors. Implementing SAAs is often done using text-based approaches that require advanced skills and experience in software programming. There are low-code development tools (LCDTs) that farmers with less programming experience can use to implement their own SAAs. The LCDTs offer different features and techniques for application development e.g., some employ form-based application specification while others use graphical drag-and-drop techniques. As such, the different LCDTs are best suited for different specific tasks. For instance, a farmer may need to implement an application that connects to sensors to receive data and generate timely notifications when set thresholds are exceeded. However, to the farmer, it can be difficult to know which kind of LCDTs to choose and what category of these tools are best suited for the task. In this paper, we contrast different LCDTs and show how to use DisCoPar to develop SAAs by non-expert programmers. As a contribution, this paper presents properties for LCDTs that can be beneficial to farmers and demonstrates how DisCoPar can be used in developing SAAs.

This paper brings into light all the new developmental work performed in the wide domain of high frequency PCBs for the realisation of innovative metasurfaces at 5GHz as well as compact highly integrated 5G antenna-in-modules at 40 GHz. There is a fast growing demand in high frequency market that justifies the intense R&D work also on microwave and especially mmWave technologies, comprising both “beyond the state-of-the-art high frequency PCBs” and advanced PCB integration concepts. In this context, this paper intends to highlight new knowledge in materials, processes as well as thermal dissipation concepts, that have been derived from various R&D projects, but especially in the framework of the FET-EU “Visorsurf” and the EU-Serena projects. In specific, R&D work will be shown on the emerging concepts of metamaterials that can be software programmable and adapt their properties. The Visorsurf main objective is the development of a hardware platform, the Hypersurface, whose electromagnetic behavior can be programmatically defined. The key enablers for this are the metasurfaces whose electromagnetic properties depend on their internal structure. The Hypersurface hardware platform will be a 4-layer build-up of high frequency PCB substrate materials with the metasurfaces on the top and custom electronic controller nodes at the bottom of the PCB hardware platform. This paper will elaborate on how innovative PCB processes have been tailored to high frequency substrates for the manufacturing of the first 4-layer Hypersurface PCB hardware platform with a size of 300mmx300mm. In a complimentary way, the paper will describe in detail new chip embedding concepts in the same family of high frequency PCB substrates toward the realization of highly miniaturized advanced packages for 5G mmWave applications at 40 GHz. These concepts show vividly the potential of PCB embedding technologies as the mean for heterogeneous integration in high frequency advanced packages/modules. The paper discusses in detail all process chain developments in high frequency PCBs for the embedding of GaN and SiGe chips in PCBs, their interconnection path concept, the embedding of passives, the fabrication of the antenna module and its stacking on a high power or low power PCB module for the final formation of a 6-layer antenna-in-module package which could be separately assembled on the system board. Furthermore, the paper will present for the first time innovative thermal dissipation concepts for the “Serena” antenna module, with the prevailing scenario of thermal vias to the bottom of the GaN and SiGe chips for direct heat removal. All processes for realization of high frequency substrates and embedded 5G 40 GHz antenna modules will be discussed in detail.

The purpose of this paper is to describe a novel fusion algorithm for multi-modal biometric identification. In this paper we describe the fusion of fingerprints and voice. This combination of biometrics is rarely used in verification systems although this biometric pair is simple to use and not too invasive. A framework for the combination of several data fusion algorithms is described. In this paper we use only two types of data fusion techniques, namely weighted sum and fuzzy system. Two independent identity decisions can be thus obtained, and from them two beliefs that the identity is verified can be derived. The two beliefs are combined using Dempster-Shafer's approach to obtain the final decision. The results are reported by ROC curves.

This paper presents an energy arbitrage strategy of a lithium-ion Battery Storage System (BSS) in sequential Day-ahead and Intraday (DA+INT) markets, considering its Cycle Aging Cost (CAC). One of the critical queries of the BSS in such problems is how to tackle the risk of uncertain prices in both market floors. Towards this end, a financial risk management method, i.e., Second-Order Stochastic Dominance Constraints (SOSDCs), is used to control the risk of uncertain market prices. Despite the promising performance of the SOSDCs over a broad range of decision-making problems, the primary challenge for decision-makers taking advantage of this approach is the selection of the minimum profit threshold. To effectively overcome this obstacle, this paper proposes a new benchmark selection approach based on a fuzzy decision-making manner over in-sample and out-of-sample analyses. The idea behind considering both in-sample and out-of-sample studies lies in an unforeseen change of the results by setting various benchmarks in SOSDCs. In this regard, to precisely formulate this problem, with an eye on the battery CAC, a linear two-stage stochastic framework is suggested. The numerical results show the applicability of the developed approach in benchmark selection for the SOSDCs.

OP VVV Electrical Engineering Technologies with High-Level of Embedded Intelligence CZ.02.1.01/0.0/0.0/18_069/0009855, project No. SGS-2021-021. This paper presents a new zero current switching bidirectional DC-DC converter for dual voltage automotive systems. The improved version of hard-switched DC-DC converter with an additional auxiliary resonant network, which is a combination of two auxiliary switches, two resonant inductors and two capacitors. The operating characteristics of this converter design is to transfer the power flow from low voltage to high voltage and vice-versa. The soft-turn on (ZCS) and soft-turn off (ZCS) operations are achieved by using two auxiliary resonant networks. In addition to that, the auxiliary switches obtain ZCS turn-on/ turn off operations, since additional losses are negligible. Minimized switching losses and improved efficiency are significant merits in this topology. The operation principles and design simulation analysis are reported in this paper in order to validate the theoretical analysis. The simulation of converter design is performed on 100/350V/500W converter system operated at 75 kHz switching frequency.

This paper proposes a high-efficiency two-stage isolated dc-dc converter for applications with wide variations of output voltages. It employs a first, pre-regulation stage and a second stage based on an LLC converter, integrated with the first. The second stage is always operated at resonance, ensuring maximum efficiency. The first pre-regulation stage is in charge of i) achieving the desired overall conversion ratio and of ii) always achieving soft-switching operation of all the switches, even in presence of wide output voltage variations. This allows to tackle the typical challenge of keeping conversion loss low even with input or output port voltages that may vary in a wide range. In this paper, the considered conversion structure is shown considering a preliminary experimental prototype that interfaces a 750-V dc-link with an output bus of nominal voltage in the range 250 V to 500 V, which is common in electric vehicle battery-charging applications. The considered preliminary prototype is rated 5 kW and achieves a peak efficiency of 97.5% at 3 kW output power.